{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Poiseuille Pipe (velocity-Neumann)\n", "\n", "As for the case of a Poiseuille channel flow, this simulation is used as test for the outlet boundary condition with a fixed pressure. A velocity Bounce-Back BC is used at inlet, to provide an increase of pressure at inlet. The fixed pressure at outlet is meant to avoid a constant increase of the domain average density." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:17.165299Z", "iopub.status.busy": "2026-06-19T18:47:17.165115Z", "iopub.status.idle": "2026-06-19T18:47:18.355165Z", "shell.execute_reply": "2026-06-19T18:47:18.353980Z" } }, "outputs": [], "source": [ "from nassu.cfg.model import ConfigScheme\n", "\n", "filename = \"validation/analytical/03_poiseuille_pipe_flow/03_poiseuille_pipe_flow.nassu.yaml\"\n", "\n", "sim_cfgs = ConfigScheme.sim_cfgs_from_file_dct(filename)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The simulation parameters are shown below" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:18.358054Z", "iopub.status.busy": "2026-06-19T18:47:18.357784Z", "iopub.status.idle": "2026-06-19T18:47:18.375904Z", "shell.execute_reply": "2026-06-19T18:47:18.374892Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Ntautime_steps
0320.5132000
\n", "
" ], "text/plain": [ " N tau time_steps\n", "0 32 0.51 32000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "from nassu.cfg.schemes.simul import SimulationConfigs\n", "\n", "dct = {\"N\": [], \"tau\": [], \"time_steps\": []}\n", "\n", "\n", "def add_to_dict(sim_cfg: SimulationConfigs):\n", " dct[\"N\"].append(sim_cfg.domain.domain_size.y)\n", " dct[\"tau\"].append(sim_cfg.models.LBM.tau)\n", " dct[\"time_steps\"].append(sim_cfg.n_steps)\n", "\n", "\n", "sim_cfg = next(\n", " sim_cfg\n", " for (name, _), sim_cfg in sim_cfgs.items()\n", " if name.startswith(\"velocityNeumannPoiseuillePipeMultilevel\")\n", ")\n", "add_to_dict(sim_cfg)\n", "\n", "df = pd.DataFrame(dct, index=None)\n", "\n", "df" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In this case, the IBM domain limits for the $x$-direction must be set such that the the body is sufficiently far from domain's boundaries. Otherwise, numerical instability may be found." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions to use for processing of poiseuille pipe." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:18.425073Z", "iopub.status.busy": "2026-06-19T18:47:18.424889Z", "iopub.status.idle": "2026-06-19T18:47:19.898772Z", "shell.execute_reply": "2026-06-19T18:47:19.897864Z" } }, "outputs": [], "source": [ "from typing import Callable\n", "\n", "import numpy as np\n", "\n", "import nassu.viz as common\n", "\n", "common.use_style()\n", "\n", "\n", "def get_poiseuille_pipe_analytical_func() -> Callable:\n", " \"\"\"Poiseuille analytical velocity function\n", "\n", " Returns:\n", " Callable[[float], float]: Analytical velocity function\n", " \"\"\"\n", " return lambda r: 2 * (1 - r * r)\n", "\n", "\n", "def get_poiseuille_pipe_numerical_avg_vel(ux_vals: np.ndarray) -> float:\n", " # Average velocity is ~half the maximun velocity.\n", " # Numerical integration gives worse results for average velocity\n", " return np.max(ux_vals) / 2\n", "\n", "\n", "def get_pos_values_inside_pipe(sim_cfg: SimulationConfigs) -> np.ndarray:\n", " body = sim_cfg.domain.bodies[\"cylinder\"]\n", " scale, translation = sim_cfg.domain.export_rescale_components(None, \"cylinder geometry\", 3)\n", " lnas = body.get_lnas_rescaled(scale, translation)\n", " vertices = lnas.geometry.vertices\n", "\n", " x_val = sim_cfg.domain.domain_size.x * 3 // 4 + 2\n", " z_val = sim_cfg.domain.domain_size.z / 2\n", " min_y, max_y = (vertices[:, 1].min(), vertices[:, 1].max())\n", " min_y, max_y = int(np.floor(min_y)), int(np.ceil(max_y))\n", "\n", " p1, p2 = (x_val, min_y, z_val), (x_val, max_y, z_val)\n", " line = np.linspace(p1, p2, num=max_y - min_y, endpoint=False)\n", " return line\n", "\n", "\n", "def get_pos_values_along_pipe(sim_cfg: SimulationConfigs) -> np.ndarray:\n", " min_x, max_x = 0, sim_cfg.domain.domain_size.x - 1\n", " y_val = sim_cfg.domain.domain_size.y / 2\n", " z_val = sim_cfg.domain.domain_size.z / 2\n", "\n", " p1, p2 = (min_x, y_val, z_val), (max_x, y_val, z_val)\n", " line = np.linspace(p1, p2, num=max_x - min_x, endpoint=False)\n", " return line\n", "\n", "\n", "def plot_analytical_poiseuille_pipe_vels(ax):\n", " x = np.arange(\n", " -1,\n", " 1.01,\n", " 0.01,\n", " )\n", " analytical_func = get_poiseuille_pipe_analytical_func()\n", " analytical_data = analytical_func(x)\n", " ax.plot(x, analytical_data, **common.markers.exp_line(linestyle=\"--\"), label=\"Analytical\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Results\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the velocity profile from simulation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:19.901645Z", "iopub.status.busy": "2026-06-19T18:47:19.901263Z", "iopub.status.idle": "2026-06-19T18:47:19.999293Z", "shell.execute_reply": "2026-06-19T18:47:19.998151Z" } }, "outputs": [], "source": [ "import numpy as np\n", "from vtkmodules.util.numpy_support import vtk_to_numpy\n", "\n", "extracted_data = {}\n", "array_to_extract = \"ux\"\n", "\n", "export_instantaneous_cfg = sim_cfg.output.exports\n", "macr_export = export_instantaneous_cfg[\"default\"].volumes[\"default\"].inst\n", "data = macr_export.read_export(sim_cfg.n_steps)\n", "\n", "pos = get_pos_values_inside_pipe(sim_cfg)\n", "\n", "# Sum 0.5 because data is cell data, so it's in the center of the cell\n", "p1 = pos[0] + 0.5\n", "p2 = pos[-1] + 0.5\n", "\n", "line = common.create_line(p1, p2, len(pos) - 1)\n", "\n", "probe_filter = common.probe_over_line(line, data)\n", "\n", "probed_data = vtk_to_numpy(probe_filter.GetOutput().GetPointData().GetArray(array_to_extract))\n", "extracted_data = {\"pos\": pos, \"data\": probed_data}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract velocity along the pipe" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:20.001466Z", "iopub.status.busy": "2026-06-19T18:47:20.001268Z", "iopub.status.idle": "2026-06-19T18:47:20.006328Z", "shell.execute_reply": "2026-06-19T18:47:20.005567Z" } }, "outputs": [], "source": [ "ux_along_pipe = {}\n", "\n", "pos = get_pos_values_along_pipe(sim_cfg)\n", "# Sum 0.5 because data is cell data, so it's in the center of the cell\n", "p1 = pos[0] + 0.5\n", "p2 = pos[-1] + 0.5\n", "\n", "line = common.create_line(p1, p2, len(pos) - 1)\n", "\n", "probe_filter = common.probe_over_line(line, data)\n", "\n", "probed_data = vtk_to_numpy(probe_filter.GetOutput().GetPointData().GetArray(array_to_extract))\n", "ux_along_pipe = {\"pos\": pos, \"data\": probed_data}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The velocity profile at the end of simulation is compared with the steady state analytical solution below:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:20.008835Z", "iopub.status.busy": "2026-06-19T18:47:20.008652Z", "iopub.status.idle": "2026-06-19T18:47:20.228703Z", "shell.execute_reply": "2026-06-19T18:47:20.227571Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = common.fig_single()\n", "\n", "\n", "def normalize_pos(pos):\n", " # Normalize between -1 and 1\n", " pos -= pos.min()\n", " pos /= pos.max()\n", " pos -= 0.5\n", " pos *= 2\n", "\n", "\n", "num_data = extracted_data\n", "num_avg_vel = get_poiseuille_pipe_numerical_avg_vel(extracted_data[\"data\"])\n", "pos_norm = extracted_data[\"pos\"][:, 1].copy()\n", "R = pos_norm.max() - pos_norm.min()\n", "normalize_pos(pos_norm)\n", "\n", "ax.plot(\n", " pos_norm,\n", " extracted_data[\"data\"] / num_avg_vel,\n", " **common.markers.sim(shape=\"o\", alpha=0.8),\n", " label=f\"R={R} (avg. {num_avg_vel:.2e})\",\n", ")\n", "\n", "plot_analytical_poiseuille_pipe_vels(ax)\n", "ax.set_title(f\"Poiseuille Pipe\\n({sim_cfg.models.LBM.vel_set} {sim_cfg.models.LBM.coll_oper})\")\n", "ax.legend()\n", "plt.tight_layout()\n", "plt.show(fig)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Good agreement was also obtained for this case, with an coherent flow development." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:20.230827Z", "iopub.status.busy": "2026-06-19T18:47:20.230634Z", "iopub.status.idle": "2026-06-19T18:47:20.809147Z", "shell.execute_reply": "2026-06-19T18:47:20.808309Z" } }, "outputs": [ { "data": { "image/jpeg": "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", "image/png": "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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pyvista as pv\n", "\n", "array_to_inspect = \"ux\"\n", "\n", "time_step = macr_export.time_steps(sim_cfg.n_steps)[-1]\n", "xdmf_reader = pv.get_reader(str(macr_export.xdmf_filename))\n", "xdmf_reader.set_active_time_value(float(time_step))\n", "multi_block = xdmf_reader.read()\n", "\n", "sliced_blocks = multi_block.slice(\n", " normal=[1, 0, 0], origin=[3 * sim_cfg.domain.domain_size.x // 4, 0, 0]\n", ")\n", "\n", "plotter = pv.Plotter(window_size=(600, 500))\n", "sliced_blocks.set_active_scalars(array_to_inspect)\n", "plotter.add_mesh(sliced_blocks, cmap=\"coolwarm\")\n", "plotter.show(jupyter_backend=\"static\", cpos=\"yz\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The sectional view of the flow profile shows a axissymmetric flow with a secundary flow ocurring between the IBM body and the boundaries." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:20.811439Z", "iopub.status.busy": "2026-06-19T18:47:20.811250Z", "iopub.status.idle": "2026-06-19T18:47:21.049979Z", "shell.execute_reply": "2026-06-19T18:47:21.048937Z" } }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, ax = common.fig_single()\n", "\n", "ax.plot(\n", " ux_along_pipe[\"pos\"][:, 0],\n", " ux_along_pipe[\"data\"],\n", " **common.markers.sim_line(linestyle=\"--\"),\n", ")\n", "ax.set_title(r\"$u_x$ at $(x, y=0.5, z=0.5)$\")\n", "\n", "ax.set_ylabel(\"$u_x$\")\n", "ax.set_xlabel(\"$x$\")\n", "plt.tight_layout()\n", "plt.show(fig)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The centerline velocity is shown above. It presents asymptotic decay as for the turbulent channel. However, at the end of IBM domain limits, the flow suffers an expansion and the centerline velocity reduces." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Version" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:21.052021Z", "iopub.status.busy": "2026-06-19T18:47:21.051835Z", "iopub.status.idle": "2026-06-19T18:47:21.142701Z", "shell.execute_reply": "2026-06-19T18:47:21.141746Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Version: 2.0.1a0\n", "Commit hash: 7c0d788ec9503d2706dadf0db3acd36d61fae57e\n" ] } ], "source": [ "sim_info = sim_cfg.output.read_info()\n", "\n", "nassu_commit = sim_info[\"commit\"]\n", "nassu_version = sim_info[\"version\"]\n", "print(\"Version:\", nassu_version)\n", "print(\"Commit hash:\", nassu_commit)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Configuration" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2026-06-19T18:47:21.144540Z", "iopub.status.busy": "2026-06-19T18:47:21.144360Z", "iopub.status.idle": "2026-06-19T18:47:21.292162Z", "shell.execute_reply": "2026-06-19T18:47:21.291182Z" } }, "outputs": [ { "data": { "text/html": [ "
simulations:\n",
       "  - name: periodicPoiseuillePipeN16\n",
       "    save_path: ./validation/analytical/03_poiseuille_pipe_flow/results/periodic\n",
       "    n_steps: 2000\n",
       "    report:\n",
       "      frequency: 1000\n",
       "\n",
       "    data:\n",
       "      divergence: {frequency: 50}\n",
       "      exports:\n",
       "        default:\n",
       "          macrs: [rho, u, f_IBM, S]\n",
       "          interval:\n",
       "            frequency: 0\n",
       "            lvl: 0\n",
       "          target:\n",
       "            volume: {}\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "        plane_series:\n",
       "          macrs: [rho, u]\n",
       "          interval: {frequency: 2000, lvl: 0}\n",
       "          target:\n",
       "            planes:\n",
       "              # Cross-section normal to the streamwise (x) flow at mid-domain.\n",
       "              # Exported as a triangle surface so ParaView renders the parabolic\n",
       "              # pipe velocity profile as a plane instead of a point cloud. With\n",
       "              # min/max omitted the plane spans the full domain; each case below\n",
       "              # only overrides axis_pos for its own mid-domain x position.\n",
       "              cross_section:\n",
       "                axis: x\n",
       "                axis_pos: 12\n",
       "                dist: 1\n",
       "\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 24\n",
       "        y: 24\n",
       "        z: 24\n",
       "      block_size: 8\n",
       "\n",
       "      bodies:\n",
       "        cylinder:\n",
       "          geometry_path: fixture/stl/basic/cylinder.stl\n",
       "          small_triangles: add\n",
       "          transformation:\n",
       "            scale: [8, 8, 8]\n",
       "            translation: [-4, 4, 4]\n",
       "\n",
       "    models:\n",
       "      precision:\n",
       "        default: single\n",
       "\n",
       "      LBM:\n",
       "        tau: 0.8\n",
       "        F:\n",
       "          x: 6.25E-05\n",
       "          y: 0\n",
       "          z: 0\n",
       "        vel_set: D3Q27\n",
       "        coll_oper: RRBGK\n",
       "\n",
       "      engine:\n",
       "        name: CUDA\n",
       "\n",
       "      IBM:\n",
       "        forces_accomodate_time: 1000\n",
       "        body_cfgs:\n",
       "          default: {}\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [true, false, false]\n",
       "        BC_map:\n",
       "          - pos: N\n",
       "            BC: RegularizedHWBB\n",
       "            wall_normal: N\n",
       "            order: 1\n",
       "\n",
       "          - pos: S\n",
       "            BC: RegularizedHWBB\n",
       "            wall_normal: S\n",
       "            order: 1\n",
       "\n",
       "          - pos: F\n",
       "            BC: RegularizedHWBB\n",
       "            wall_normal: F\n",
       "            order: 2\n",
       "\n",
       "          - pos: B\n",
       "            BC: RegularizedHWBB\n",
       "            wall_normal: B\n",
       "            order: 2\n",
       "\n",
       "  - name: periodicPoiseuillePipeN32\n",
       "    parent: periodicPoiseuillePipeN16\n",
       "\n",
       "    n_steps: 8000\n",
       "\n",
       "    data:\n",
       "      exports:\n",
       "        plane_series:\n",
       "          target:\n",
       "            planes:\n",
       "              # Mid-domain x for this domain size; bounds auto-span the full domain.\n",
       "              cross_section: {axis_pos: 20}\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 40\n",
       "        y: 40\n",
       "        z: 40\n",
       "      block_size: 8\n",
       "      bodies: !not-inherit\n",
       "        cylinder:\n",
       "          geometry_path: fixture/stl/basic/cylinder.stl\n",
       "          small_triangles: add\n",
       "          transformation:\n",
       "            scale: [16, 16, 16]\n",
       "            translation: [-4, 4, 4]\n",
       "\n",
       "    models:\n",
       "      LBM:\n",
       "        F:\n",
       "          x: 7.8125E-06\n",
       "          y: 0\n",
       "          z: 0\n",
       "\n",
       "  - name: periodicPoiseuillePipeN64\n",
       "    parent: periodicPoiseuillePipeN16\n",
       "\n",
       "    n_steps: 32000\n",
       "\n",
       "    data:\n",
       "      exports:\n",
       "        plane_series:\n",
       "          target:\n",
       "            planes:\n",
       "              # Mid-domain x for this domain size; bounds auto-span the full domain.\n",
       "              cross_section: {axis_pos: 36}\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 72\n",
       "        y: 72\n",
       "        z: 72\n",
       "      block_size: 8\n",
       "      bodies: !not-inherit\n",
       "        cylinder:\n",
       "          geometry_path: fixture/stl/basic/cylinder.stl\n",
       "          small_triangles: add\n",
       "          transformation:\n",
       "            scale: [32, 32, 32]\n",
       "            translation: [-4, 4, 4]\n",
       "    models:\n",
       "      LBM:\n",
       "        F:\n",
       "          x: 9.76563E-07\n",
       "          y: 0\n",
       "          z: 0\n",
       "\n",
       "  - name: periodicPoiseuillePipeN128\n",
       "    parent: periodicPoiseuillePipeN16\n",
       "\n",
       "    n_steps: 128000\n",
       "\n",
       "    data:\n",
       "      exports:\n",
       "        plane_series:\n",
       "          target:\n",
       "            planes:\n",
       "              # Mid-domain x for this domain size; bounds auto-span the full domain.\n",
       "              cross_section: {axis_pos: 68}\n",
       "\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 136\n",
       "        y: 136\n",
       "        z: 136\n",
       "      block_size: 8\n",
       "      bodies: !not-inherit\n",
       "        cylinder:\n",
       "          geometry_path: fixture/stl/basic/cylinder.stl\n",
       "          small_triangles: add\n",
       "          transformation:\n",
       "            scale: [64, 64, 64]\n",
       "            translation: [-4, 4, 4]\n",
       "    models:\n",
       "      LBM:\n",
       "        F:\n",
       "          x: 1.22070E-07\n",
       "          y: 0\n",
       "          z: 0\n",
       "\n",
       "  - name: velocityNeumannPoiseuillePipeMultilevel\n",
       "    save_path: ./validation/analytical/03_poiseuille_pipe_flow/results/velocity_neumann_multilevel\n",
       "\n",
       "    n_steps: 32000\n",
       "\n",
       "    report:\n",
       "      frequency: 1000\n",
       "\n",
       "    data:\n",
       "      divergence: {frequency: 1}\n",
       "      exports:\n",
       "        default:\n",
       "          macrs: [rho, u, f_IBM, S]\n",
       "          interval:\n",
       "            frequency: 8000\n",
       "            lvl: 0\n",
       "          target:\n",
       "            volume: {}\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "        plane_series:\n",
       "          macrs: [rho, u]\n",
       "          interval: {frequency: 8000, lvl: 0}\n",
       "          target:\n",
       "            planes:\n",
       "              # Cross-section normal to the streamwise (x) flow; bounds omitted so\n",
       "              # the plane auto-spans the full domain on the in-plane axes.\n",
       "              cross_section:\n",
       "                axis: x\n",
       "                axis_pos: 52\n",
       "                dist: 1\n",
       "\n",
       "          outputs:\n",
       "            instantaneous: true\n",
       "    domain:\n",
       "      domain_size:\n",
       "        x: 104\n",
       "        y: 32\n",
       "        z: 32\n",
       "\n",
       "      block_size: 8\n",
       "      bodies_domain_limits:\n",
       "        start: [4, 8, 8]\n",
       "        end: [88, 40, 40]\n",
       "        is_abs: true\n",
       "      bodies:\n",
       "        cylinder:\n",
       "          geometry_path: fixture/stl/basic/cylinder.stl\n",
       "          small_triangles: add\n",
       "          transformation:\n",
       "            scale: [8, 8, 8]\n",
       "            translation: [4, 8, 8]\n",
       "      refinement:\n",
       "        static:\n",
       "          default:\n",
       "            bodies:\n",
       "              - body_name: cylinder\n",
       "                lvl: 1\n",
       "                normal_offsets: [-2, 0, 2]\n",
       "\n",
       "    models:\n",
       "      precision:\n",
       "        default: single\n",
       "\n",
       "      LBM:\n",
       "        tau: 0.51\n",
       "        vel_set: D3Q27\n",
       "        coll_oper: RRBGK\n",
       "\n",
       "      engine:\n",
       "        name: CUDA\n",
       "\n",
       "      BC:\n",
       "        periodic_dims: [false, false, false]\n",
       "        BC_map:\n",
       "          - pos: W\n",
       "            BC: UniformFlow\n",
       "            wall_normal: W\n",
       "            rho: 1.0\n",
       "            ux: 0.05\n",
       "            uy: 0\n",
       "            uz: 0\n",
       "            order: 2\n",
       "\n",
       "          - pos: E\n",
       "            BC: RegularizedNeumannOutlet\n",
       "            rho: 1.0\n",
       "            wall_normal: E\n",
       "            order: 2\n",
       "\n",
       "          - pos: N\n",
       "            BC: Neumann\n",
       "            wall_normal: N\n",
       "            order: 1\n",
       "\n",
       "          - pos: S\n",
       "            BC: Neumann\n",
       "            wall_normal: S\n",
       "            order: 1\n",
       "\n",
       "          - pos: F\n",
       "            BC: Neumann\n",
       "            wall_normal: F\n",
       "            order: 0\n",
       "\n",
       "          - pos: B\n",
       "            BC: Neumann\n",
       "            wall_normal: B\n",
       "            order: 0\n",
       "\n",
       "      IBM:\n",
       "        forces_accomodate_time: 1000\n",
       "        body_cfgs:\n",
       "          default: {}\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{n+nt}{simulations}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicPoiseuillePipeN16}\n", "\\PY{+w}{ }\\PY{n+nt}{save\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{./validation/analytical/03\\PYZus{}poiseuille\\PYZus{}pipe\\PYZus{}flow/results/periodic}\n", "\\PY{+w}{ }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2000}\n", "\\PY{+w}{ }\\PY{n+nt}{report}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1000}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{data}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{divergence}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{50}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{n+nt}{exports}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{default}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{rho}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{u}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{f\\PYZus{}IBM}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{S}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{interval}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{target}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{volume}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{n+nt}{outputs}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{instantaneous}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n", "\\PY{+w}{ }\\PY{n+nt}{plane\\PYZus{}series}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{rho}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{u}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{interval}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{2000}\\PY{p+pIndicator}{,}\\PY{n+nt}{ lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{n+nt}{target}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{planes}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} Cross\\PYZhy{}section normal to the streamwise (x) flow at mid\\PYZhy{}domain.}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} Exported as a triangle surface so ParaView renders the parabolic}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} pipe velocity profile as a plane instead of a point cloud. With}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} min/max omitted the plane spans the full domain; each case below}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} only overrides axis\\PYZus{}pos for its own mid\\PYZhy{}domain x position.}\n", "\\PY{+w}{ }\\PY{n+nt}{cross\\PYZus{}section}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{axis}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{x}\n", "\\PY{+w}{ }\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{12}\n", "\\PY{+w}{ }\\PY{n+nt}{dist}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{outputs}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{instantaneous}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n", "\\PY{+w}{ }\\PY{n+nt}{domain}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{domain\\PYZus{}size}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{24}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{24}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{24}\n", "\\PY{+w}{ }\\PY{n+nt}{block\\PYZus{}size}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{bodies}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{cylinder}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/basic/cylinder.stl}\n", "\\PY{+w}{ }\\PY{n+nt}{small\\PYZus{}triangles}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{add}\n", "\\PY{+w}{ }\\PY{n+nt}{transformation}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{scale}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{8}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{\\PYZhy{}4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{]}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{models}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{precision}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{default}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{single}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{LBM}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{tau}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.8}\n", "\\PY{+w}{ }\\PY{n+nt}{F}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{6.25E\\PYZhy{}05}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{vel\\PYZus{}set}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{D3Q27}\n", "\\PY{+w}{ }\\PY{n+nt}{coll\\PYZus{}oper}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RRBGK}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{engine}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{CUDA}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{IBM}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{forces\\PYZus{}accomodate\\PYZus{}time}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1000}\n", "\\PY{+w}{ }\\PY{n+nt}{body\\PYZus{}cfgs}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{default}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{periodic\\PYZus{}dims}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{true}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{false}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{false}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{BC\\PYZus{}map}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{N}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RegularizedHWBB}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{N}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{S}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RegularizedHWBB}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{S}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{F}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RegularizedHWBB}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{F}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{B}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RegularizedHWBB}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{B}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicPoiseuillePipeN32}\n", "\\PY{+w}{ }\\PY{n+nt}{parent}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicPoiseuillePipeN16}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8000}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{data}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{exports}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{plane\\PYZus{}series}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{target}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{planes}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} Mid\\PYZhy{}domain x for this domain size; bounds auto\\PYZhy{}span the full domain.}\n", "\\PY{+w}{ }\\PY{n+nt}{cross\\PYZus{}section}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{20}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{domain}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{domain\\PYZus{}size}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{40}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{40}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{40}\n", "\\PY{+w}{ }\\PY{n+nt}{block\\PYZus{}size}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n", "\\PY{+w}{ }\\PY{n+nt}{bodies}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!not\\PYZhy{}inherit}\n", "\\PY{+w}{ }\\PY{n+nt}{cylinder}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/basic/cylinder.stl}\n", "\\PY{+w}{ }\\PY{n+nt}{small\\PYZus{}triangles}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{add}\n", "\\PY{+w}{ }\\PY{n+nt}{transformation}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{scale}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{16}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{16}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{16}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{\\PYZhy{}4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{]}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{models}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{LBM}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{F}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{7.8125E\\PYZhy{}06}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicPoiseuillePipeN64}\n", "\\PY{+w}{ }\\PY{n+nt}{parent}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicPoiseuillePipeN16}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{32000}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{data}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{exports}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{plane\\PYZus{}series}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{target}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{planes}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} Mid\\PYZhy{}domain x for this domain size; bounds auto\\PYZhy{}span the full domain.}\n", "\\PY{+w}{ }\\PY{n+nt}{cross\\PYZus{}section}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{36}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{domain}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{domain\\PYZus{}size}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{72}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{72}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{72}\n", "\\PY{+w}{ }\\PY{n+nt}{block\\PYZus{}size}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n", "\\PY{+w}{ }\\PY{n+nt}{bodies}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!not\\PYZhy{}inherit}\n", "\\PY{+w}{ }\\PY{n+nt}{cylinder}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/basic/cylinder.stl}\n", "\\PY{+w}{ }\\PY{n+nt}{small\\PYZus{}triangles}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{add}\n", "\\PY{+w}{ }\\PY{n+nt}{transformation}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{scale}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{32}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{32}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{32}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{\\PYZhy{}4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{models}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{LBM}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{F}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{9.76563E\\PYZhy{}07}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicPoiseuillePipeN128}\n", "\\PY{+w}{ }\\PY{n+nt}{parent}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{periodicPoiseuillePipeN16}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{128000}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{data}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{exports}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{plane\\PYZus{}series}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{target}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{planes}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} Mid\\PYZhy{}domain x for this domain size; bounds auto\\PYZhy{}span the full domain.}\n", "\\PY{+w}{ }\\PY{n+nt}{cross\\PYZus{}section}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{68}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{domain}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{domain\\PYZus{}size}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{136}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{136}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{136}\n", "\\PY{+w}{ }\\PY{n+nt}{block\\PYZus{}size}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n", "\\PY{+w}{ }\\PY{n+nt}{bodies}\\PY{p}{:}\\PY{+w}{ }\\PY{k+kt}{!not\\PYZhy{}inherit}\n", "\\PY{+w}{ }\\PY{n+nt}{cylinder}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/basic/cylinder.stl}\n", "\\PY{+w}{ }\\PY{n+nt}{small\\PYZus{}triangles}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{add}\n", "\\PY{+w}{ }\\PY{n+nt}{transformation}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{scale}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{64}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{64}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{64}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{\\PYZhy{}4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{4}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{models}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{LBM}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{F}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.22070E\\PYZhy{}07}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{velocityNeumannPoiseuillePipeMultilevel}\n", "\\PY{+w}{ }\\PY{n+nt}{save\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{./validation/analytical/03\\PYZus{}poiseuille\\PYZus{}pipe\\PYZus{}flow/results/velocity\\PYZus{}neumann\\PYZus{}multilevel}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{n\\PYZus{}steps}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{32000}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{report}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1000}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{data}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{divergence}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{1}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{n+nt}{exports}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{default}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{rho}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{u}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{f\\PYZus{}IBM}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{S}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{interval}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8000}\n", "\\PY{+w}{ }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{target}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{volume}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{n+nt}{outputs}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{instantaneous}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n", "\\PY{+w}{ }\\PY{n+nt}{plane\\PYZus{}series}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{macrs}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{rho}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{u}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{interval}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{n+nt}{frequency}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{8000}\\PY{p+pIndicator}{,}\\PY{n+nt}{ lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\\PY{+w}{ }\\PY{n+nt}{target}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{planes}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} Cross\\PYZhy{}section normal to the streamwise (x) flow; bounds omitted so}\n", "\\PY{+w}{ }\\PY{c+c1}{\\PYZsh{} the plane auto\\PYZhy{}spans the full domain on the in\\PYZhy{}plane axes.}\n", "\\PY{+w}{ }\\PY{n+nt}{cross\\PYZus{}section}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{axis}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{x}\n", "\\PY{+w}{ }\\PY{n+nt}{axis\\PYZus{}pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{52}\n", "\\PY{+w}{ }\\PY{n+nt}{dist}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{outputs}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{instantaneous}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n", "\\PY{+w}{ }\\PY{n+nt}{domain}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{domain\\PYZus{}size}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{x}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{104}\n", "\\PY{+w}{ }\\PY{n+nt}{y}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{32}\n", "\\PY{+w}{ }\\PY{n+nt}{z}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{32}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{block\\PYZus{}size}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{8}\n", "\\PY{+w}{ }\\PY{n+nt}{bodies\\PYZus{}domain\\PYZus{}limits}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{start}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{end}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{88}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{40}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{40}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{is\\PYZus{}abs}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{true}\n", "\\PY{+w}{ }\\PY{n+nt}{bodies}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{cylinder}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{geometry\\PYZus{}path}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{fixture/stl/basic/cylinder.stl}\n", "\\PY{+w}{ }\\PY{n+nt}{small\\PYZus{}triangles}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{add}\n", "\\PY{+w}{ }\\PY{n+nt}{transformation}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{scale}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{8}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{translation}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{4}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{8}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{refinement}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{static}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{default}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{bodies}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{body\\PYZus{}name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{cylinder}\n", "\\PY{+w}{ }\\PY{n+nt}{lvl}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n", "\\PY{+w}{ }\\PY{n+nt}{normal\\PYZus{}offsets}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{\\PYZhy{}2}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{0}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{2}\\PY{p+pIndicator}{]}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{models}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{precision}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{default}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{single}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{LBM}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{tau}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.51}\n", "\\PY{+w}{ }\\PY{n+nt}{vel\\PYZus{}set}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{D3Q27}\n", "\\PY{+w}{ }\\PY{n+nt}{coll\\PYZus{}oper}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RRBGK}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{engine}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{name}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{CUDA}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{periodic\\PYZus{}dims}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{[}\\PY{n+nv}{false}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{false}\\PY{p+pIndicator}{,}\\PY{+w}{ }\\PY{n+nv}{false}\\PY{p+pIndicator}{]}\n", "\\PY{+w}{ }\\PY{n+nt}{BC\\PYZus{}map}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{W}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{UniformFlow}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{W}\n", "\\PY{+w}{ }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.0}\n", "\\PY{+w}{ }\\PY{n+nt}{ux}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0.05}\n", "\\PY{+w}{ }\\PY{n+nt}{uy}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{uz}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{E}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{RegularizedNeumannOutlet}\n", "\\PY{+w}{ }\\PY{n+nt}{rho}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1.0}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{E}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{2}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{N}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Neumann}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{N}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{S}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Neumann}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{S}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{F}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Neumann}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{F}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\n", "\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZhy{}}\\PY{+w}{ }\\PY{n+nt}{pos}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{B}\n", "\\PY{+w}{ }\\PY{n+nt}{BC}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{Neumann}\n", "\\PY{+w}{ }\\PY{n+nt}{wall\\PYZus{}normal}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{B}\n", "\\PY{+w}{ }\\PY{n+nt}{order}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{0}\n", "\n", "\\PY{+w}{ }\\PY{n+nt}{IBM}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{forces\\PYZus{}accomodate\\PYZus{}time}\\PY{p}{:}\\PY{+w}{ }\\PY{l+lScalar+lScalarPlain}{1000}\n", "\\PY{+w}{ }\\PY{n+nt}{body\\PYZus{}cfgs}\\PY{p}{:}\n", "\\PY{+w}{ }\\PY{n+nt}{default}\\PY{p}{:}\\PY{+w}{ }\\PY{p+pIndicator}{\\PYZob{}}\\PY{p+pIndicator}{\\PYZcb{}}\n", "\\end{Verbatim}\n" ], "text/plain": [ "simulations:\n", " - name: periodicPoiseuillePipeN16\n", " save_path: ./validation/analytical/03_poiseuille_pipe_flow/results/periodic\n", " n_steps: 2000\n", " report:\n", " frequency: 1000\n", "\n", " data:\n", " divergence: {frequency: 50}\n", " exports:\n", " default:\n", " macrs: [rho, u, f_IBM, S]\n", " interval:\n", " frequency: 0\n", " lvl: 0\n", " target:\n", " volume: {}\n", " outputs:\n", " instantaneous: true\n", " plane_series:\n", " macrs: [rho, u]\n", " interval: {frequency: 2000, lvl: 0}\n", " target:\n", " planes:\n", " # Cross-section normal to the streamwise (x) flow at mid-domain.\n", " # Exported as a triangle surface so ParaView renders the parabolic\n", " # pipe velocity profile as a plane instead of a point cloud. With\n", " # min/max omitted the plane spans the full domain; each case below\n", " # only overrides axis_pos for its own mid-domain x position.\n", " cross_section:\n", " axis: x\n", " axis_pos: 12\n", " dist: 1\n", "\n", " outputs:\n", " instantaneous: true\n", " domain:\n", " domain_size:\n", " x: 24\n", " y: 24\n", " z: 24\n", " block_size: 8\n", "\n", " bodies:\n", " cylinder:\n", " geometry_path: fixture/stl/basic/cylinder.stl\n", " small_triangles: add\n", " transformation:\n", " scale: [8, 8, 8]\n", " translation: [-4, 4, 4]\n", "\n", " models:\n", " precision:\n", " default: single\n", "\n", " LBM:\n", " tau: 0.8\n", " F:\n", " x: 6.25E-05\n", " y: 0\n", " z: 0\n", " vel_set: D3Q27\n", " coll_oper: RRBGK\n", "\n", " engine:\n", " name: CUDA\n", "\n", " IBM:\n", " forces_accomodate_time: 1000\n", " body_cfgs:\n", " default: {}\n", "\n", " BC:\n", " periodic_dims: [true, false, false]\n", " BC_map:\n", " - pos: N\n", " BC: RegularizedHWBB\n", " wall_normal: N\n", " order: 1\n", "\n", " - pos: S\n", " BC: RegularizedHWBB\n", " wall_normal: S\n", " order: 1\n", "\n", " - pos: F\n", " BC: RegularizedHWBB\n", " wall_normal: F\n", " order: 2\n", "\n", " - pos: B\n", " BC: RegularizedHWBB\n", " wall_normal: B\n", " order: 2\n", "\n", " - name: periodicPoiseuillePipeN32\n", " parent: periodicPoiseuillePipeN16\n", "\n", " n_steps: 8000\n", "\n", " data:\n", " exports:\n", " plane_series:\n", " target:\n", " planes:\n", " # Mid-domain x for this domain size; bounds auto-span the full domain.\n", " cross_section: {axis_pos: 20}\n", "\n", " domain:\n", " domain_size:\n", " x: 40\n", " y: 40\n", " z: 40\n", " block_size: 8\n", " bodies: !not-inherit\n", " cylinder:\n", " geometry_path: fixture/stl/basic/cylinder.stl\n", " small_triangles: add\n", " transformation:\n", " scale: [16, 16, 16]\n", " translation: [-4, 4, 4]\n", "\n", " models:\n", " LBM:\n", " F:\n", " x: 7.8125E-06\n", " y: 0\n", " z: 0\n", "\n", " - name: periodicPoiseuillePipeN64\n", " parent: periodicPoiseuillePipeN16\n", "\n", " n_steps: 32000\n", "\n", " data:\n", " exports:\n", " plane_series:\n", " target:\n", " planes:\n", " # Mid-domain x for this domain size; bounds auto-span the full domain.\n", " cross_section: {axis_pos: 36}\n", "\n", " domain:\n", " domain_size:\n", " x: 72\n", " y: 72\n", " z: 72\n", " block_size: 8\n", " bodies: !not-inherit\n", " cylinder:\n", " geometry_path: fixture/stl/basic/cylinder.stl\n", " small_triangles: add\n", " transformation:\n", " scale: [32, 32, 32]\n", " translation: [-4, 4, 4]\n", " models:\n", " LBM:\n", " F:\n", " x: 9.76563E-07\n", " y: 0\n", " z: 0\n", "\n", " - name: periodicPoiseuillePipeN128\n", " parent: periodicPoiseuillePipeN16\n", "\n", " n_steps: 128000\n", "\n", " data:\n", " exports:\n", " plane_series:\n", " target:\n", " planes:\n", " # Mid-domain x for this domain size; bounds auto-span the full domain.\n", " cross_section: {axis_pos: 68}\n", "\n", " domain:\n", " domain_size:\n", " x: 136\n", " y: 136\n", " z: 136\n", " block_size: 8\n", " bodies: !not-inherit\n", " cylinder:\n", " geometry_path: fixture/stl/basic/cylinder.stl\n", " small_triangles: add\n", " transformation:\n", " scale: [64, 64, 64]\n", " translation: [-4, 4, 4]\n", " models:\n", " LBM:\n", " F:\n", " x: 1.22070E-07\n", " y: 0\n", " z: 0\n", "\n", " - name: velocityNeumannPoiseuillePipeMultilevel\n", " save_path: ./validation/analytical/03_poiseuille_pipe_flow/results/velocity_neumann_multilevel\n", "\n", " n_steps: 32000\n", "\n", " report:\n", " frequency: 1000\n", "\n", " data:\n", " divergence: {frequency: 1}\n", " exports:\n", " default:\n", " macrs: [rho, u, f_IBM, S]\n", " interval:\n", " frequency: 8000\n", " lvl: 0\n", " target:\n", " volume: {}\n", " outputs:\n", " instantaneous: true\n", " plane_series:\n", " macrs: [rho, u]\n", " interval: {frequency: 8000, lvl: 0}\n", " target:\n", " planes:\n", " # Cross-section normal to the streamwise (x) flow; bounds omitted so\n", " # the plane auto-spans the full domain on the in-plane axes.\n", " cross_section:\n", " axis: x\n", " axis_pos: 52\n", " dist: 1\n", "\n", " outputs:\n", " instantaneous: true\n", " domain:\n", " domain_size:\n", " x: 104\n", " y: 32\n", " z: 32\n", "\n", " block_size: 8\n", " bodies_domain_limits:\n", " start: [4, 8, 8]\n", " end: [88, 40, 40]\n", " is_abs: true\n", " bodies:\n", " cylinder:\n", " geometry_path: fixture/stl/basic/cylinder.stl\n", " small_triangles: add\n", " transformation:\n", " scale: [8, 8, 8]\n", " translation: [4, 8, 8]\n", " refinement:\n", " static:\n", " default:\n", " bodies:\n", " - body_name: cylinder\n", " lvl: 1\n", " normal_offsets: [-2, 0, 2]\n", "\n", " models:\n", " precision:\n", " default: single\n", "\n", " LBM:\n", " tau: 0.51\n", " vel_set: D3Q27\n", " coll_oper: RRBGK\n", "\n", " engine:\n", " name: CUDA\n", "\n", " BC:\n", " periodic_dims: [false, false, false]\n", " BC_map:\n", " - pos: W\n", " BC: UniformFlow\n", " wall_normal: W\n", " rho: 1.0\n", " ux: 0.05\n", " uy: 0\n", " uz: 0\n", " order: 2\n", "\n", " - pos: E\n", " BC: RegularizedNeumannOutlet\n", " rho: 1.0\n", " wall_normal: E\n", " order: 2\n", "\n", " - pos: N\n", " BC: Neumann\n", " wall_normal: N\n", " order: 1\n", "\n", " - pos: S\n", " BC: Neumann\n", " wall_normal: S\n", " order: 1\n", "\n", " - pos: F\n", " BC: Neumann\n", " wall_normal: F\n", " order: 0\n", "\n", " - pos: B\n", " BC: Neumann\n", " wall_normal: B\n", " order: 0\n", "\n", " IBM:\n", " forces_accomodate_time: 1000\n", " body_cfgs:\n", " default: {}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Code\n", "\n", "Code(filename=filename)" ] } ], "metadata": { "kernelspec": { "display_name": "nassu (3.12.13)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.13" } }, "nbformat": 4, "nbformat_minor": 2 }